The invention relates to a method of comparing two stereo images to determine a location of an object or point in a field of view.
It is well-known that the position of an object in a volume can be determined using two spaced apart cameras. Both cameras take an image of the object at the same time or nearly the same time. Then the images are compared to determine the location in each image of a point or series of points on the object. From that information one can calculate the location of the object in the volume such that each point on the object has a known and different x, y, z coordinate.
Today there are algorithms which allow computers to perform image matching of two stereo images. Typical images from a video camera contain a 640xc3x97480 array of pixels. In a xe2x80x9cblack and whitexe2x80x9d image each pixel will have a gray scale value of from 0 to 255. Current algorithms use the gray scale values to perform pixel comparisons to identify the position of an object in one image with respect to the other image. Although this method is quite accurate, substantial computer capacity is needed to perform the image matching and the process is relatively slow. As a result more expensive computer hardware is needed to do stereo image processing. Thus, one must either use expensive image processing hardware to achieve near real time processing or be satisfied with the slow processing speeds that occur with off the shelf computing components such as a personal computer (PC). Consequently, there is a need for a method of determining the position of an object from stereo images which is fast and can be performed on a low cost computer.
For many years the art has used the Laplacian pyramid to process and compress images as part of stereo processing. Compressed images are easier to store and transmit. When an image is subjected to a series of Laplacian transforms via pyramid processing the image becomes successively smaller dimensionally; however, the gray scale information remains at 8 bits. Each higher level array is half the dimensions of its predecessor. Prior to the present invention the art used these full gray scale Laplacian images for stereo image correlation which requires much computational complexity. Yet, I have found that by reducing the gray scale dimensionality of the Laplacian images I can correlate stereo images significantly faster using a simple processor.
I provide a method for correlating two stereo images in which the images are subjected to a Laplacian operator to produce reduced grayscale Laplacian images in which the pixels have a value of +1, 0 or xe2x88x921. Then, I overlap the two images to produce pairs of overlapping pixels. The values of the two overlapping pixels are summed in a manner so that if both pixels are +1 or both are xe2x88x921 the summed value is +1, if one pixel is +1 and the other pixel is xe2x88x921, the resulting sum is xe2x88x921 and if one or both pixels are zero the resulting sum is zero. All of the correlation values for the regions about the two overlapping pixels are combined to get a correlation value for the pair of pixels that overlap generating a correlation image. Then, the two Laplacian images are shifted relative to one another and correlation values are computed for each pair of pixels for this particular overlap. This process is repeated several times resulting in correlation images for each overlap. The overlap which has the highest correlation value is the best match for that pixel. Having determined the best match for each pixel, one can then determine the location of an object or point in the field of view using standard stereo processing techniques. Other objects and advantages of the method will become apparent from a description of certain present preferred embodiments illustrated in the drawings.